---
title: Nemotron LoRA Fine-tuning
emoji: 🚀
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false
license: apache-2.0
---

# 🚀 LoRA Fine-tuning for Nemotron-3-8B

Train Nvidia's Nemotron-3-8B model with LoRA on your custom datasets.

## Features

- ✅ 4-bit Quantization with QLoRA
- ✅ PEFT Integration
- ✅ Gradio UI
- ✅ Auto Push to Hub
- ✅ Progress Tracking

## Quick Start

1. Upgrade to GPU (Settings → Hardware)
2. Configure training parameters
3. Add HF token if needed
4. Click "Start Training"

## Configuration

Default settings are optimized for A10G (24GB VRAM). Adjust for your GPU:

- **T4 (16GB)**: batch_size=2, gradient_accumulation=8
- **A10G (24GB)**: batch_size=4, gradient_accumulation=4
- **A100 (40GB)**: batch_size=8, gradient_accumulation=2

## Dataset Format

Supports multiple formats (auto-detected):
- Q&A: `{"question": "...", "answer": "..."}`
- Instruction: `{"instruction": "...", "response": "..."}`
- Text: `{"text": "..."}`

## Using the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("nvidia/nemotron-3-8b-base-4k")
model = PeftModel.from_pretrained(base_model, "YOUR_USERNAME/nemotron-lora")
tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/nemotron-lora")
```

## Support

For issues: [GitHub](https://github.com/YOUR_REPO)